Shoulder viscoelasticity in a raptor-inspired model alleviates instability and enhances passive gust rejection

Recent experiments with gliding raptors reveal a perplexing dichotomy: remarkably resilient gust rejection, but, at the same time, an exceptionally high degree of longitudinal instability. To resolve this incompatibility, a multiple degree of freedom model is developed with minimal requisite complexity to examine the hypothesis that the bird shoulder joint may embed essential stabilizing and preflexive mechanisms for rejecting rapid perturbations while simplifying and reducing control effort. Thus, the formulation herein is centrally premised upon distinct wing pitch and body pitch angles coupled via a Kelvin-Voigt viscoelastic shoulder joint. The model accurately exhibits empirical gust response of an unstable gliding raptor, generates biologically plausible equilibrium configurations, and the viscoelastic shoulder coupling is shown to drastically alleviate the high degree of instability predicted by conventional linear flight dynamics models. In fact, stability analysis of the model predicts a critical system timescale (the time to double amplitude of a pitch divergence mode) that is commensurate within vivomeasured latency of barn owls (Tyto alba). Active gust mitigation is studied by presupposing the owl behaves as an optimal controller. The system is under-actuated and the feedback control law is resolved in the controllable subspace using a Kalman decomposition. Importantly, control-theoretic analysis precisely identifies what discrete gust frequencies may be rapidly and passively rejected versus disturbances requiring feedback control intervention.

Joint extension speed dictates bio-inspired morphing trajectories for optimal longitudinal flight dynamics

Avian wing morphing allows dynamic, active control of complex flight manoeuvres. Previous linear time-invariant (LTI) models have quantified the effect of varying fixed wing configurations but the time-dependent effects of morphing between different configurations is not well understood. To fill this gap, I implemented a linear parameter-varying (LPV) model for morphing wing gull flight. This approach models the wing joint angles as scheduled parameters and accounts for nonlinear kinematic and gravitational effects while interpolating between LTI models at discrete trim points. With the resulting model, I investigated the longitudinal response associated with various joint extension trajectories. By optimizing the extension trajectory for four independent objectives (speed and pitch angle overshoot, speed rise time and pitch angle settling time), I found that the extension trajectory inherent to the gull wing does not guarantee an optimal response but may provide a sufficient response with a simpler mechanical implementation. Furthermore, the results indicated that gulls likely require extension speed feedback. This morphing LPV model provides insights into underlying control mechanisms, which may allow for avian-like flight in future highly manoeuvrable uncrewed aerial vehicles.

Mosquitoes escape looming threats by actively flying with the bow wave induced by the attacker

To detect and escape looming threats, night-flying insects must rely on other senses than vision alone. Nocturnal mosquitoes can evade looming objects in the dark, but how they achieve this is still unknown. Here, we show how night-active female malaria mosquitoes escape from rapidly looming objects that simulate defensive actions of blood-hosts. First, we quantified the escape performance of flying mosquitoes from an event-triggered mechanical swatter, showing that mosquitoes use swatter-induced airflow to increase their escape success. Secondly, we used high-speed videography and deep-learning-based tracking to analyze escape flights in detail, showing that mosquitoes use banked turns to evade the threat. By combining escape kinematics data with numerical simulations of attacker-induced airflow and a mechanistic movement model, we unraveled how mosquitoes control these banked evasive maneuvers: they actively steer away from the danger, and then passively travel with the bow wave produced by the attacker. Our results demonstrate that night-flying mosquitoes can detect looming objects when visual cues are minimal, suggesting that they use attacker-induced airflow both to detect the danger and as a fluid medium to move with away from the threat. This shows that escape strategies of flying insects are more complex than previous visually induced escape flight studies suggest. As most insects are of similar or smaller sizes than mosquitoes, comparable escape strategies are expected among millions of flying insect species. The here-observed escape maneuvers are distinct from those of mosquitoes escaping from odor-baited traps, thus providing new insights for the development of novel trapping techniques for integrative vector management.

Wing Kinematics-Based Flight Control Strategy in Insect-Inspired Flight Systems: Deep Reinforcement Learning Gives Solutions and Inspires Controller Design in Flapping MAVs

Flying insects exhibit outperforming stability and control via continuous wing flapping even under severe disturbances in various conditions of wind gust and turbulence. While conventional linear proportional derivative (PD)-based controllers are widely employed in insect-inspired flight systems, they usually fail to deal with large perturbation conditions in terms of the 6-DoF nonlinear control strategy. Here we propose a novel wing kinematics-based controller, which is optimized based on deep reinforcement learning (DRL) to stabilize bumblebee hovering under large perturbations. A high-fidelity Open AI Gym environment is established through coupling a CFD data-driven aerodynamic model and a 6-DoF flight dynamic model. The control policy with an action space of 4 is optimized using the off-policy Soft Actor-Critic (SAC) algorithm with automating entropy adjustment, which is verified to be of feasibility and robustness to achieve fast stabilization of the bumblebee hovering flight under full 6-DoF large disturbances. The 6-DoF wing kinematics-based DRL control strategy may provide an efficient autonomous controller design for bioinspired flapping-wing micro air vehicles.

Wavelet-Based Identification for Spinning Projectile with Gasodynamic Control Aerodynamic Coefficients Determination

Identification of a spinning projectile controlled with gasodynamic engines is shown in this paper. A missile model with a measurement inertial unit was developed from Newton’s law of motion and its aerodynamic coefficients were identified. This was achieved by applying the maximum likelihood principle in the wavelet domain. To assess the results, this was also performed in the time domain. The outcomes were obtained for two cases: when noise was not present and when it was included in the data. In all cases, the identification was performed in the passive mode, i.e., no special system identification experiments were designed. In the noise-free case, aerodynamic coefficients were estimated with high accuracy. When noise was included in the data, the wavelet-based estimates had a drop in their accuracy, but were still very accurate, whereas for the time domain approach the estimates were considered inaccurate.

A stability perspective of bioinspired unmanned aerial vehicles performing optimal dynamic soaring

The phenomenon of dynamic soaring, as exhibited by soaring birds, has long been a biological inspiration for aerospace and control engineers. If this fascinating phenomenon, which allows soaring birds to perform almost unpowered flight using wind shear, can be mimicked by unmanned aerial vehicles (UAVs), then there is substantial potential for technological and economic enhancement of UAV performance. Although there has been a considerable amount of research covering the modeling, optimization, control and simulation aspects of different UAVs performing dynamic soaring, there is little to no conclusive work analyzing the stability of such UAVs in soaring orbits. In this paper we present a comprehensive framework for determining the stability of soaring UAVs utilizing both linear (Floquet-based) and nonlinear (contraction theory-based) techniques. Floquet stability analysis was inconclusive, which led to the use of a nonlinear contraction formulation to reach a conclusive stability assessment for an actual nonlinear fixed-wing UAV performing dynamic soaring. Furthermore, parametric variation along with numerical simulations were conducted to ascertain the response of the actual nonlinear system when perturbed from the nominal motion studied in this paper. The analysis and simulations revealed that the system possesses instability as the UAV motion diverges from its nominal trajectory and follows an undesirable path. From this result we conclude, for the first time in the literature as far as we are aware, that UAVs performing dynamic soaring in an optimal way to reduce wind shear requirements are inherently unstable. The results of this work suggest that mimicking of dynamic soaring by UAVs will require careful investigation of tracking and regulatory controls that need to be implemented.

Absence of "selfish herd" dynamics in bird flocks under threat

The "selfish herd" hypothesis¹ provides a potential mechanism to explain a ubiquitous phenomenon in nature: that of non-kin aggregations. Individuals in selfish herds are thought to benefit by reducing their own risk at the expense of conspecifics by attracting toward their neighbors' positions^1,2 or central locations in the aggregation.^3-5 Alternatively, increased alignment with their neighbors' orientation could reduce the chance of predation through information sharing^6-8 or collective escape.⁶ Using both small and large flocks of homing pigeons (Columba livia; n = 8-10 or n = 27-34 individuals) tagged with 5-Hz GPS loggers and a GPS-tagged, remote-controlled model peregrine falcon (Falco peregrinus), we tested whether individuals increase their use of attraction over alignment when under perceived threat. We conducted n = 27 flights in treatment conditions, chased by the robotic "predator," and n = 16 flights in control conditions (not chased). Despite responding strongly to the RobotFalcon-by turning away from its flight direction-individuals in treatment flocks demonstrated no increased attraction compared with control flocks, and this result held across both flock sizes. We suggest that mutualistic alignment is more advantageous than selfish attraction in groups with a high coincidence of individual and collective interests (adaptive hypothesis). However, we also explore alternative explanations, such as high cognitive demand under threat and collision avoidance (mechanistic hypotheses). We conclude that selfish herd may not be an appropriate paradigm for understanding the function of highly synchronous collective motion, as observed in bird flocks and perhaps also fish shoals and highly aligned mammal aggregations, such as moving herds.

Sensor Fusion Algorithm Using a Model-Based Kalman Filter for the Position and Attitude Estimation of Precision Aerial Delivery Systems

In this research, we focus on the use of Unmanned Aerial Vehicles (UAVs) for the delivery of payloads and navigation towards safe-landing zones, specifically on the modeling of flight dynamics of lightweight vehicles denoted Precision Aerial Delivery Systems (PADSs). While a wide range of nonlinear models has been developed and tested on high-end applications considering various degrees of freedom (DOF), linear models suitable for low-cost applications have not been explored thoroughly. In this study, we propose and compare two linear models, a linearized version of a 6-DOF model specifically developed for micro-lightweight systems, and an alternative model based on a double integrator. Both linear models are implemented with a sensor fusion algorithm using a Kalman filter to estimate the position and attitude of PADSs, and their performance is compared to a nonlinear 6-DOF model. Simulation results demonstrate that both models, when incorporated into a Kalman filter estimation scheme, can determine the flight dynamics of PADSs during smooth flights. While it is validated that the double integrator model can adequately operate under the proposed estimation scheme for up to small acceleration changes, the linearized model proves to be capable of reproducing the nonlinear model characteristics even during moderately steep turns.

Preliminary Design of a Model-Free Synthetic Sensor for Aerodynamic Angle Estimation for Commercial Aviation

Heterogeneity of the small aircraft category (e.g., small air transport (SAT), urban air mobility (UAM), unmanned aircraft system (UAS)), modern avionic solution (e.g., fly-by-wire (FBW)) and reduced aircraft (A/C) size require more compact, integrated, digital and modular air data system (ADS) able to measure data from the external environment. The MIDAS project, funded in the frame of the Clean Sky 2 program, aims to satisfy those recent requirements with an ADS certified for commercial applications. The main pillar lays on a smart fusion between COTS solutions and analytical sensors (patented technology) for the identification of the aerodynamic angles. The identification involves both flight dynamic relationships and data-driven state observer(s) based on neural techniques, which are deterministic once the training is completed. As this project will bring analytical sensors on board of civil aircraft as part of a redundant system for the very first time, design activities documented in this work have a particular focus on airworthiness certification aspects. At this maturity level, simulated data are used, real flight test data will be used in the next stages. Data collection is described both for the training and test aspects. Training maneuvers are defined aiming to excite all dynamic modes, whereas test maneuvers are collected aiming to validate results independently from the training set and all autopilot configurations. Results demonstrate that an alternate solution is possible enabling significant savings in terms of computational effort and lines of codes but they show, at the same time, that a better training strategy may be beneficial to cope with the new neural network architecture.

Dynamics and flight control of a flapping-wing robotic insect in the presence of wind gusts

With the goal of operating a biologically inspired robot autonomously outside of laboratory conditions, in this paper, we simulated wind disturbances in a laboratory setting and investigated the effects of gusts on the flight dynamics of a millimetre-scale flapping-wing robot. Simplified models describing the disturbance effects on the robot's dynamics are proposed, together with two disturbance rejection schemes capable of estimating and compensating for the disturbances. The proposed methods are experimentally verified. The results show that these strategies reduced the root-mean-square position errors by more than 50% when the robot was subject to 80 cm s^-1 horizontal wind. The analysis of flight data suggests that modulation of wing kinematics to stabilize the flight in the presence of wind gusts may indirectly contribute an additional stabilizing effect, reducing the time-averaged aerodynamic drag experienced by the robot. A benchtop experiment was performed to provide further support for this observed phenomenon.

Echolocating bats use future-target information for optimal foraging

When seeing or listening to an object, we aim our attention toward it. While capturing prey, many animal species focus their visual or acoustic attention toward the prey. However, for multiple prey items, the direction and timing of attention for effective foraging remain unknown. In this study, we adopted both experimental and mathematical methodology with microphone-array measurements and mathematical modeling analysis to quantify the attention of echolocating bats that were repeatedly capturing airborne insects in the field. Here we show that bats select rational flight paths to consecutively capture multiple prey items. Microphone-array measurements showed that bats direct their sonar attention not only to the immediate prey but also to the next prey. In addition, we found that a bat's attention in terms of its flight also aims toward the next prey even when approaching the immediate prey. Numerical simulations revealed a possibility that bats shift their flight attention to control suitable flight paths for consecutive capture. When a bat only aims its flight attention toward its immediate prey, it rarely succeeds in capturing the next prey. These findings indicate that bats gain increased benefit by distributing their attention among multiple targets and planning the future flight path based on additional information of the next prey. These experimental and mathematical studies allowed us to observe the process of decision making by bats during their natural flight dynamics.

Vision-based flight control in the hawkmoth Hyles lineata

Vision is a key sensory modality for flying insects, playing an important role in guidance, navigation and control. Here, we use a virtual-reality flight simulator to measure the optomotor responses of the hawkmoth Hyles lineata, and use a published linear-time invariant model of the flight dynamics to interpret the function of the measured responses in flight stabilization and control. We recorded the forces and moments produced during oscillation of the visual field in roll, pitch and yaw, varying the temporal frequency, amplitude or spatial frequency of the stimulus. The moths' responses were strongly dependent upon contrast frequency, as expected if the optomotor system uses correlation-type motion detectors to sense self-motion. The flight dynamics model predicts that roll angle feedback is needed to stabilize the lateral dynamics, and that a combination of pitch angle and pitch rate feedback is most effective in stabilizing the longitudinal dynamics. The moths' responses to roll and pitch stimuli coincided qualitatively with these functional predictions. The moths produced coupled roll and yaw moments in response to yaw stimuli, which could help to reduce the energetic cost of correcting heading. Our results emphasize the close relationship between physics and physiology in the stabilization of insect flight.

Active and passive stabilization of body pitch in insect flight

Flying insects have evolved sophisticated sensory–motor systems, and here we argue that such systems are used to keep upright against intrinsic flight instabilities. We describe a theory that predicts the instability growth rate in body pitch from flapping-wing aerodynamics and reveals two ways of achieving balanced flight: active control with sufficiently rapid reactions and passive stabilization with high body drag. By glueing magnets to fruit flies and perturbing their flight using magnetic impulses, we show that these insects employ active control that is indeed fast relative to the instability. Moreover, we find that fruit flies with their control sensors disabled can keep upright if high-drag fibres are also attached to their bodies, an observation consistent with our prediction for the passive stability condition. Finally, we extend this framework to unify the control strategies used by hovering animals and also furnish criteria for achieving pitch stability in flapping-wing robots.

Nonlinear flight dynamics and stability of hovering model insects

Current analyses on insect dynamic flight stability are based on linear theory and limited to small disturbance motions. However, insects' aerial environment is filled with swirling eddies and wind gusts, and large disturbances are common. Here, we numerically solve the equations of motion coupled with the Navier-Stokes equations to simulate the large disturbance motions and analyse the nonlinear flight dynamics of hovering model insects. We consider two representative model insects, a model hawkmoth (large size, low wingbeat frequency) and a model dronefly (small size, high wingbeat frequency). For small and large initial disturbances, the disturbance motion grows with time, and the insects tumble and never return to the equilibrium state; the hovering flight is inherently (passively) unstable. The instability is caused by a pitch moment produced by forward/backward motion and/or a roll moment produced by side motion of the insect.